Chunk Parsing. Steven Bird Ewan Klein Edward Loper. University of Melbourne, AUSTRALIA. University of Edinburgh, UK. University of Pennsylvania, USA

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1 Chunk Parsing Steven Bird Ewan Klein Edward Loper University of Melbourne, AUSTRALIA University of Edinburgh, UK University of Pennsylvania, USA March 1, 2012

2 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

3 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

4 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

5 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

6 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

7 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

8 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

9 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

10 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

11 chunk parsing: efficient and robust approach to parsing natural language a popular alternative to the full parsing chunks: non-overlapping regions of text contain a head word (e.g. noun) adjacent modifiers and function words motivations: extract information ignore information

12 Extracting Information: Coreference Annotation

13 Extracting Information: Message Understanding

14 Ignoring Information: Lexical Acquisition studying syntactic patterns, e.g. finding verbs in a corpus, displaying possible arguments e.g. gave, in 100 files of the Penn Treebank corpus replaced internal details of each noun phrase with NP gave NP gave up NP in NP gave NP up gave NP help gave NP to NP use in lexical acquisition, grammar development

15 Analogy with Tokenization and Tagging fundamental in NLP: segmentation and labelling tokenization and tagging other similarities: skipping material; finite-state; application specific

16 Chunking vs Parsing 1 Parsing [ ] 2 Chunking: [ G.K. Chesterton ], [ [ author ] of [ [ The Man ] who was [ Thursday ] ] ] [ G.K. Chesterton ], [ author ] of [ The Man ] who was [ Thursday ]

17 Perfection is unattainable 1. Prepositional phrase: [ [ I ] [ turned ] [ off the spectroroute ] ] 2. Verb-particle construction: [ [ I ] [ turned off ] [ the spectroroute ] ]

18 Tag Representation

19 Tree Representation

20 Chunk Structures (S: (NP: I ) saw (NP: the big dog ) on (NP: the hill )) Demonstration: reading chunk structures from Treebank and CoNLL-2000 corpora

21 Chunk Parsing regular expressions over part-of-speech tags: parse.regexpchunk Tag string: a string consisting of tags delimited with angle-brackets, e.g. <DT><JJ><NN><VBD><DT><NN> Tag pattern: regular expression over tag strings <DT><JJ>?<NN> <NN JJ>+ <NN.*> chunk a sequence of words matching a tag pattern: parse.chunkrule >>> grammar = "NP: {<DT NN>+} # Chunk sequences

22 A Simple NP Chunker import nltk grammar = r""" NP: {<DT>?<JJ>*<NN>} # chunk determiners, adjectives and nouns {<NNP>+} # chunk sequences of proper nouns """ cp = nltk.regexpparser(grammar) tagged_tokens = [("the", "DT"), ("little", "JJ"), ("cat", "NN"), ("sat", "VBD"), ("on", "IN"), ("the", "DT"), ("mat", "NN")] >>> print cp.parse(tagged_tokens) (S (NP the/dt little/jj cat/nn) sat/vbd on/in (NP the/dt mat/nn))

23 Developing Chunkers cp1 = nltk.regexpparser(r""" NP: {<DT><JJ><NN>} # Chunk det+adj+noun {<DT NN>+} # Chunk sequences of NN and DT """) cp2 = nltk.regexpparser(r""" NP: {<DT NN>+} # Chunk sequences of NN and DT {<DT><JJ><NN>} # Chunk det+adj+noun """) >>> print cp1.parse(tagged_tokens, trace=1) # Input: <DT> <JJ> <NN> <VBD> <IN> <DT> <NN> # Chunk det+adj+noun: {<DT> <JJ> <NN>} <VBD> <IN> <DT> <NN> # Chunk sequences of NN and DT: {<DT> <JJ> <NN>} <VBD> <IN> {<DT> <NN>} tracing; rule ordering; overlapping contexts

24 More Chunking Rules: Chinking chink: sequence of stopwords chinking: process of removing tokens from a chunk Entire chunk Middle of a chunk End of a chunk Input: [a/dt big/jj cat/nn] [a/dt big/jj cat/nn] [a/dt big/jj cat/nn] Operation: Chink a/dt big/jj cat/nn Chink big/jj Chink cat/nn Output: a/dt big/jj cat/nn [a/dt] big/jj [cat/nn] [a/dt big/jj] cat/nn

25 Chinking Example >>> grammar = r"""... NP:... {<.*>+} # Chunk everything... }<VBD IN>+{ # Chink sequences of VBD and IN... """ >>> cp = nltk.regexpparser(grammar) >>> print cp.parse(tagged_tokens) (S: (NP: ( the, DT ) ( little, JJ ) ( cat, NN )) ( sat, VBD ) ( on, IN ) (NP: ( the, DT ) ( mat, NN ))) >>> print nltk.chunk.accuracy(cp, nltk.corpus.conll2000.chunked_se

26 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

27 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

28 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

29 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

30 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

31 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

32 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

33 Evaluating Chunk Parsers Process: 1 take some already chunked text 2 strip off the chunks 3 rechunk it 4 compare the result with the original chunked text ChunkScore.score() precision: what fraction of the returned chunks were correct? recall: what fraction of correct chunks were returned?

34 Precision and Recall

35 Chunker evaluation in NLTK >>> chunkscore = nltk.chunk.chunkscore() >>> for chunk_struct in nltk.corpus.treebank.chunked_sents()[:10]:... test_sent = cp.parse(chunk_struct.leaves())... chunkscore.score(chunk_struct, test_sent) >>> print chunkscore ChunkParse score: Precision: 48.6% Recall: 34.0% F-Measure: 40.0%

36 Error Analysis: Missed Chunks >>> from random import randint >>> missed = chunkscore.missed() >>> for i in range(15):... print missed[randint(0,len(missed)-1)] (( A, DT ), ( Lorillard, NNP ), ( spokewoman, NN )) (( it, PRP ),) (( symptoms, NNS ),) (( even, RB ), ( brief, JJ ), ( exposures, NNS )) (( its, PRP$ ), ( Micronite, NN ), ( cigarette, NN ), ( filters (( 30, CD ), ( years, NNS )) (( workers, NNS ),) (( preliminary, JJ ), ( findings, NNS )) (( Medicine, NNP ),) (( cancer, NN ), ( deaths, NNS )) (( Consolidated, NNP ), ( Gold, NNP ), ( Fields, NNP ), ( PLC, (( Medicine, NNP ),) (( its, PRP$ ), ( Micronite, NN ), ( cigarette, NN ), ( filters (( a, DT ), ( forum, NN )) (( researchers, NNS ),)

37 Error Analysis: Incorrect Chunks >>> incorrect = chunkscore.incorrect() >>> for i in range(15):... print incorrect[randint(0,len(incorrect)-1)] (( New, JJ ), ( York-based, JJ )) (( Micronite, NN ), ( cigarette, NN )) (( a, DT ), ( forum, NN ), ( likely, JJ )) (( later, JJ ),) (( later, JJ ),) (( brief, JJ ),) (( preliminary, JJ ),) (( New, JJ ), ( York-based, JJ )) (( resilient, JJ ),) (( group, NN ),) (( cancer, NN ),) (( the, DT ),) (( cancer, NN ),) (( Micronite, NN ), ( cigarette, NN )) (( A, DT ),)

38 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

39 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

40 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

41 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

42 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

43 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

44 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

45 approaches different rules and combinations hand-crafted vs automatic focus on diagnosis: manual utility functions error analysis evaluation Development Methodology

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